consequences of individuals’ fit at work: a meta … · consequences of individuals’ fit at...

PERSONNEL PSYCHOLOGY2005, 58, 281–342

CONSEQUENCES OF INDIVIDUALS’ FIT AT WORK:A META-ANALYSIS OF PERSON–JOB,PERSON–ORGANIZATION, PERSON–GROUP,AND PERSON–SUPERVISOR FIT

AMY L. KRISTOF-BROWN, RYAN D. ZIMMERMAN, ERIN C. JOHNSONHenry B. Tippie College of Business

Department of Management & OrganizationsUniversity of Iowa

This meta-analysis investigated the relationships between person–job(PJ), person–organization (PO), person–group, and person–supervisorfit with preentry (applicant attraction, job acceptance, intent to hire,job offer) and postentry individual-level criteria (attitudes, performance,withdrawal behaviors, strain, tenure). A search of published articles,conference presentations, dissertations, and working papers yielded 172usable studies with 836 effect sizes. Nearly all of the credibility intervalsdid not include 0, indicating the broad generalizability of the relation-ships across situations. Various ways in which fit was conceptualized andmeasured, as well as issues of study design, were examined as modera-tors to these relationships in studies of PJ and PO fit. Interrelationshipsbetween the various types of fit are also meta-analyzed. 25 studies usingpolynomial regression as an analytic technique are reviewed separately,because of their unique approach to assessing fit. Broad themes emerg-ing from the results are discussed to generate the implications for futureresearch on fit.

Theories of person-environment (PE) interaction have been prevalentin the management literature for almost 100 years (e.g., Ekehammer, 1974;Lewin, 1935; Murray, 1938; Parsons, 1909; Pervin, 1968), making it “oneof the more venerable lines of psychological theorizing” (Dawis, 1992). Itis against this interactionist backdrop that the concept of PE fit emerged.Described as a “syndrome with many manifestations” (Schneider, 2001,p. 142), PE fit is broadly defined as the compatibility between an individualand a work environment that occurs when their characteristics are wellmatched. Despite, or perhaps because of, the simplicity of this definition,several distinct types of fit have garnered attention. Much emphasis has

The authors are indebted to the encouragement & feedback provided by SAPS and FrankSchmidt.

Correspondence and requests for reprints should be addressed to Amy L. Kristof-Brown,University of Iowa, Henry B. Tippie College of Business, Department of Management& Organizations, 108 Pappajohn Business Building, Iowa City, IA 52242; [email protected].

COPYRIGHT C© 2005 BLACKWELL PUBLISHING, INC.

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been placed on the match between peoples’ interests and those of othersin a vocation (e.g., Holland, 1985). However, other types of fit, such as anindividual’s compatibility with his or her job, organization, work group,and supervisors have also emerged as important research domains.

Although research on these other types of fit has been prolific, rarelyhas it been synthesized to draw conclusions about the true impact of fiton individual-level outcomes. Some progress toward integration has beenmade. However, most reviews have been nonquantitative, not differenti-ated between various types of fit, or focused solely on single types of PEfit. Edwards (1991) provided a comprehensive qualitative review of PE fitstudies published between 1960 and 1989, but did not include a quantita-tive analysis of the strength of the reported relationships. Furthermore, hisreview did not distinguish between fit with the job, group, organization,or vocation, which have been shown to have unique relationships with at-titudes and behaviors (Cable & DeRue, 2002). Another qualitative reviewconducted almost 10 years ago by Kristof (1996) focused exclusively onthe person–organization fit literature. A portion of this literature was re-cently meta-analyzed but only for a limited range of criteria (job attitudes)and a small number of effect sizes (k = 21; Verquer, Beehr, & Wagner,2003). Furthermore, little attention has been paid to the more nascentarea of person–group fit or to related topics such as person–supervisor fit.Given the qualitative focus, limited scope, and somewhat dated nature ofthese past summaries, a quantitative analysis of various types of PE fit iswarranted.

The biggest challenge to this type of analysis is the proliferation ofconceptualizations, measures, and analytic approaches that make fit an“elusive” construct (Judge & Ferris, 1992). Fit has alternatively been con-ceptualized as similarity, need–satisfaction, and demand–ability match.Further, it has been operationalized using a variety of content dimensions,including skills, needs, preferences, values, personality traits, goals, andattitudes. Strategies for measuring fit also vary widely, from directly askingindividuals to report their perceived fit to researchers indirectly assessingfit through explicit comparisons of separately rated P and E character-istics. When indirect assessments are used, E may be the subjective en-vironment as assessed by the individual or the objective environment asdetermined by other sources. Moreover, the environment can be concep-tualized as an entity with its own unique characteristics or as an aggregateof its members’ characteristics. Sampling strategies and the potential forcommon method bias also vary widely across studies, rendering inter-pretation of some results problematic. Although this diversity poses achallenge to integration, it may also produce systematic variations in re-ported fit–outcome relationships. Much of this meta-analysis, therefore, isconcerned with articulating theory-driven rationale for moderators of the

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fit–outcome relationships and evaluating the empirical evidence regardingtheir impact.

Accordingly, the purpose of this study is to empirically summarize theexisting literature in four critical domains of PE fit: person–job, person–organization, person–group, and person–supervisor fit. Specifically, weaddress the relationship between these types of fit and individual-levelpreentry outcomes (applicant attraction, job acceptance, intent to hire,job offer) and postentry consequences (attitudes, withdrawal behaviors,strain, performance, and tenure). Delving deeper into these meta-analyticfindings, we examine multiple moderators of fit–outcome relationshipsincluding various conceptualizations, operationalizations, and measure-ment approaches, as well as study design. In addition, because of the re-cent growth in studies using a polynomial regression approach (Edwards,1993, 1994) to study fit indirectly, we also review these studies. However,because the interpretation of the multiple correlations from polynomial re-gressions is ambiguous, we describe the general forms of the relationshipsdepicted in these studies, rather than meta-analyze them. This allows us tocomprehensively review the fit literature before drawing conclusions. Inthe discussion section we review the implications of our findings, proposea coherent agenda for future research, offer methodological guidance forfuture studies of fit, and present practical guidance for how to capitalizeon the benefits of fit.

Types of Person–Environment Fit

Based in the tradition of interactional psychology, the notion of peoplebeing differentially compatible with jobs, groups, organizations, and voca-tions is almost axiomatic. The concept of PE fit has been described as, “sopervasive as to be one of, if not the dominant conceptual forces in the field”(Schneider, 2001, p. 142). It is not surprising, therefore, that managementscholars have a sustained interest in the fit between individuals and theirwork environments. Perhaps in part because of this interest, research onfit continues to be one of the most eclectic domains in management. Webriefly review the primary types of fit to establish and articulate the criteriaused for inclusion and exclusion of studies in our analyses.

Person × Environment Interactions

Interactional psychology includes numerous studies that assess PE fitas a statistical interaction between the person and environment. Person-ality may be investigated as a moderator of environmental forces, as inthe case of growth need strength moderating the job characteristics–jobsatisfaction relationship (e.g., Fried & Ferris, 1987; Loher, Noe, Moeller,


& Fitzgerald, 1985), or situations may be viewed as moderators of thepersonality–outcome relationships (e.g., Colbert, Mount, Harter, Witt, &Barrick, 2004; Turban, Lau, Ngo, Chow, & Si, 2001). Despite their concep-tual appeal, moderator studies rarely conceptualize the person and environ-ment on commensurate dimensions. Without this standard it is impossibleto directly compare P and E values, a fundamental property of the PEfit theory (Edwards, Caplan, & Harrison, 1998; French, Rogers & Cobb,1974). Thus, we address only studies that measure P and E on commen-surate dimensions. Even when commensurate dimensions are used, thereare additional complications with P × E interaction studies (e.g., Arnaud,Ambrose, & Schminke, 2002; Goodman & Syvantek, 1999; Judge & Bretz,1992; Lievens, Decaesteker, & Coetsier, 2001). First, multiple correlationsthat are typically reported as effect sizes include main effects as well asinteraction effects, rendering them inconsistent with other fit measures.Second, “the interaction between person and environment variables doesnot reflect their proximity to one another” (Edwards et al., 1998, p. 41), anecessary precondition for fit.

Person-Vocation Fit

PE fit research is generally characterized by matching individuals tovarious levels of their work environment (Judge & Ferris, 1992; Kristof,1996). The broadest of these levels is the vocation or occupation. Thisresearch on person–vocation (P–V) fit includes vocational choice theo-ries that propose matching people with careers that meet their interests(e.g., Holland, 1985; Parsons, 1909; Super, 1953), and the theory of workadjustment (Dawis & Lofquist, 1984; Lofquist & Dawis, 1969), which em-phasizes that adjustment and satisfaction are the result of employees’ needsbeing met by their occupational environment. Because this research hasbeen extensively reviewed by others (Assouline & Meir, 1987; Spokane,1985; Tranberg, Slane, & Ekeberg, 1993), we do not include it in ouranalyses.

Person–job Fit

Instead, we emphasize an area closely related to PV fit but defined morenarrowly as the relationship between a person’s characteristics and thoseof the job or tasks that are performed at work. This is the domain of person–job (PJ) fit (e.g., Edwards, 1991; Kristof, 1996). Edwards (1991) outlinedtwo basic conceptualizations of the PJ fit. The first is the demands-abilitiesfit, in which employees’ knowledge, skills, and abilities are commensu-rate with what the job requires. The second form of PJ fit occurs when


employees’ needs, desires, or preferences are met by the jobs that theyperform. This type of fit, often labeled needs–supplies or supplies–valuesfit, has been the emphasis of various theories of adjustment, well-being, andsatisfaction (Caplan, 1983; French, Caplan, & Harrison, 1974; Harrison,1978; Locke, 1969; Porter, 1961, 1962).

Having restricted our definition of fit to the compatibility that resultsfrom personal and environmental characteristics being well matched, itwas necessary to exclude research on related but distinct topics. Studiesof underemployment (e.g., Feldman, Leana, & Bolino, 2002), overquali-fication (e.g., Johnson & Johnson, 1999), role overload (e.g., Netemeyer,Burton, & Johnston, 1995), and relative deprivation (Feldman et al., 2002)were not included because measures of these concepts typically assess thedegree to which there is “misfit” in only one direction. Studies of met ex-pectations were also not included (e.g., Bunderson, 2001). This decisionwas based on Schneider’s reasoning that “a person’s expectations proba-bly represent some modification of his preferences based on information”(1975, p. 460). Thus, they are likely to be tempered by reality or pastexperiences, rather than reflect the individuals’ characteristics, which arethe basis for fit.

Person–Organization Fit

Research on person–organization (PO) fit, which addresses the com-patibility between people and entire organizations, is the second type of fitwe review. Beginning with Tom’s (1971) suggestion that individuals willbe most successful in organizations that share their personalities, researchhas emphasized individual–organizational similarity as the crux of PO fit.Some research has followed Tom’s (1971) operationalization of PO fit aspersonality–climate congruence (e.g., Christiansen, Villanova, & Mikulay,1997; Ryan & Schmitt, 1996); however, Chatman’s (1989) seminal the-ory of PO fit focused attention primarily on values. With the subsequentvalidation of the Organizational Culture Profile (O’Reilly, Chatman, &Caldwell, 1991), a values-based instrument, value congruence becamewidely accepted as the defining operationalization of PO fit (Kristof, 1996;Verquer et al., 2003). A lesser used but theoretically consistent operational-ization is PO goal congruence (e.g., Vancouver & Schmitt, 1991; Witt &Nye, 1992). In all cases, the emphasis is on the compatibility betweencommensurate individual and organizational characteristics.

Excluded from our analysis are studies that assess agreement on orga-nizational culture or climate. For example, studies of climate discrepancy(e.g., Joyce & Slocum, 1982) typically ask participants to describe theclimate in their organizations. Thus, they typically do not assess howwell the climate matches participants’ personal characteristics or meets


their needs, as much as the extent to which an individual’s perceptionconcurs with other’s. Similarly, climate strength studies (e.g., Schneider,Salvaggio, & Subirats, 2002) emphasize within-group consistency inclimate perceptions, rather than individual’s fit with the organizationalcharacteristics. Therefore, studies in both of these areas were not deemedrelevant to this analysis.

Person–Group Fit

The third category of research we review is person–group (PG) orperson–team fit, which focuses on the interpersonal compatibility betweenindividuals and their work groups (Judge & Ferris, 1992; Kristof, 1996;Werbel & Gilliland, 1999). Of all types of fit, PG fit research is the mostnascent. Despite high levels of interest in coworker similarity on demo-graphic variables (e.g., Riordan, 2000), little research has emphasizedhow the psychological compatibility between coworkers influences indi-vidual outcomes in group settings. Only a handful of published studieshave examined the fit on characteristics such as goals (Kristof-Brown &Stevens, 2001; Witt, 1998) or values (Adkins, Ravlin, & Meglino, 1996;Becker, 1992; Good & Nelson, 1971). There are, however, several studiesthat have examined PG fit on personality traits (Barsade, Ward, Turner,& Sonnenfeld, 2000; Hobman, Bordia, & Gallois, 2003; Kristof-Brown,Barrick, & Stevens, in press; Slocombe & Bluedorn, 1999; Strauss,Barrick, & Connerley, 2001).

These studies are distinct from research on team similarity or ho-mogeneity (e.g., Barry & Stewart, 1997; Jehn, Northcraft, & Neale,1999) and studies that aggregate individuals’ fit to the unit level (e.g.,Harrison, Shaffer, & Bhaskar, 2002; Ostroff, 1993; Salvaggio, 2004).These aggregate-level studies predict unit-level outcomes, rather thanindividual-level criteria that we emphasize. Studies by Ostroff andRothhausen (1997) and Vancouver, Millsap, and Peters (1994) demon-strate that fit–outcome relationships differ when they are assessed at higherlevels of analysis, underscoring the need to differentiate aggregate-levelfit studies from others.

Research on relational demography is also excluded from our review.Although demography assesses a person’s similarity to group members topredict individual-level outcomes, the emphasis is exclusively on demo-graphic variables (such as race, age, gender). Milliken and Martins (1996)and Harrison, Price, and Bell (1998) categorize demographic variables assurface-level or easily observable attributes and contrast them with otherdeep-level, underlying characteristics such as values or goals. Harrisonet al. (1998) demonstrated that there are meaningful differences between


similarity on these types of characteristics, and that over time it is fit ondeep-level characteristics that has the greatest impact on outcomes.

Person–Supervisor Fit

A final form of PE fit exists in the dyadic relationships between in-dividuals and others in their work environments. Although dyadic fitmay occur between coworkers (e.g., Antonioni & Park, 2001), appli-cants and recruiters (e.g., Graves & Powell, 1995), and mentors andproteges (e.g., Turban & Dougherty, 1994), by far the most well-researchedarea is the match between supervisors and subordinates (e.g., Adkins,Russel, & Werbel, 1994; Van Vianen, 2000). Given the limited numberof studies on other types of dyadic fit and the importance of supervisor–subordinate relationships on work outcomes (Griffeth, Hom, & Gaertner,2001), the final area we meta-analyze is person–supervisor (PS) fit. Stud-ies of leader–follower value congruence (e.g., Colbert, 2004; Krishnan,2002), supervisor–subordinate personality similarity (Schaubroeck &Lam, 2002), and manager–employee goal congruence (e.g., Witt, 1998) areincluded in this category. In each case, the supervisors’ personal character-istics represented the environment. Studies in which supervisors reportedwork group or organizational characteristics (e.g., Becker, 1992; Becker,Billings, Eveleth, & Gilbert, 1996) were classified as either PG or PO fit,respectively.

Related to the PS fit domain is research on leader member exchange(LMX; e.g., Graen, 1976) and perceptual similarity (e.g., Wexley &Pulakos, 1983). Although each of these areas emphasizes the dyadic in-teractions between individuals, they are excluded from this review. In thecase of LMX, the emphasis is on the nature of the relationship that de-velops between leaders and followers not the match of their underlyingpsychological characteristics. Therefore, only LMX studies that also ex-amine PS fit are included in our analyses. Studies of perceptual similarity,that is, similarity between a subordinate’s description of the manager andthe manager’s self-description were also excluded, because they assessaccuracy of perceptions rather than fit.

Moderator Analyses

Even after these exclusions, there is still remarkable heterogeneity inthe way that fit has been conceptualized and measured. A thorough under-standing of fit’s influence on individual-level outcomes must account forthese differences. Next, we provide theory-driven rationale for concep-tual and methodological issues that we expect to moderate fit–outcome


relationships, with an emphasis on PJ and PO fit because of the numberof studies available on these topics.

Conceptualizations of Fit

Complementary versus supplementary. Muchinsky and Monahan(1987, pp. 268–269) highlighted a troubling oversight in the PE fit lit-erature when they noted that although “‘person–environment congruence’refers to the degree of fit or match between the two sets of variables . . . whatexactly constitutes a fit or match is not totally clear.” In response to thisambiguity, they proposed two distinct conceptualizations of PE fit: com-plementary and supplementary fit. For complementary fit, “the basis fora good fit is the mutually offsetting pattern of relevant characteristics be-tween the person and the environment” (Muchinsky & Monahan, 1987,pp. 272). Although they operationalized this type of fit strictly as individ-uals’ skills meeting environmental needs (demands–abilities fit), Kristof(1996) expanded the definition to include when individuals’ needs are metby environmental supplies (needs–supplies fit). Thus, complementary fitoccurs when individuals’ characteristics fill a gap in the current environ-ment, or vice versa. The second major conceptualization of fit, supple-mentary fit, exists when the individual and the environment are similar.Whereas complementary conceptualizations of fit have dominated the PJfit literature, supplementary fit has been the focus of other types of fit.

Theories of need fulfillment (e.g., Locke, 1976; Rice, McFarlin, Hunt,& Near, 1985) explain the primary mechanism by which complementaryneeds–supplies fit influences attitudes. These theories share the commonproposition that people will experience more positive job attitudes whentheir needs are satisfied. Recently, it has been argued that supplementaryfit may also function through the process of need fulfillment (Van Vianen,2000). Theories such as Festinger’s theory of social comparison (1954),Heider’s balanced state theory (Heider, 1958), and Byrne’s similarity-attraction paradigm (1971) all suggest that people have a fundamentalneed for consensual validation of their perspectives, which can be met byinteracting with similar others. Therefore, achieving supplementary fit isone way to have personal needs met, but obtaining needs–supplies fit ismore direct. Therefore, we expect that supplementary fit will have a some-what weaker relationship with job attitudes than does needs–supplies fit.Consistent with this logic, demands–abilities fit should have the smallestrelationship with job attitudes because it emphasizes meeting environmen-tal, rather than individual needs.

We expect a different pattern of relationships for nonattitudinal cri-teria, such as performance, strain, or turnover. If a person does nothave the requisite abilities to meet situational demands, overall andtask performance are likely to suffer. Even contextual performance may


decline, if employees do not have adequate attentional resources (Kanfer& Ackerman, 1989) to engage in organizational citizenship behaviorsbecause they are being stretched by task demands. Strain should be highunder such conditions, and turnover may result because of consistent un-derperformance. This implies a stronger effect for demands–abilities fiton the aforementioned criteria. However, needs–supplies and supplemen-tary fit are also likely to affect these outcomes. The theory of reasonedaction suggests that attitudes toward a given object will generally resultin behaviors that are consistent with those attitudes (Ajzen & Fishbein,1977; Fishbein & Ajzen, 1975). Thus, if individuals’ needs are being metat work, resulting positive attitudes may mitigate strain, facilitate higherperformance, and reduce turnover. Nonetheless, because proficiency mat-ters to outcomes of strain, performance, and turnover, we expect that theimpact of demands–abilities fit will be greater than the other conceptual-izations of fit. Studies that include multiple conceptualizations of fit (i.e.,assess needs–supplies and demands–abilities fit) should produce strongereffects than those using single conceptualizations because they tap intomultiple mechanisms by which fit has an impact.

Content dimensions of PE fit. Further complicating research on PE fitis the variety of content dimensions used to operationalize fit. The decisionon what dimensions or characteristics to use is somewhat determined by thebroader conceptualization of fit being explored. For example, a demands–abilities fit is almost exclusively based on the content dimensions of indi-viduals’ KSAs and job or organizational demands. However, with grow-ing recognition of the importance of personality on work performance(Barrick & Mount, 1991), a few studies have expanded the demands-abilities fit to include personality traits and values (e.g., Edwards, 1996;Lauver & Kristof-Brown, 2001). Similarly, a needs–supplies fit, which hastraditionally emphasized individuals’ needs and preferences, can also beconsidered to include value preferences, or what types of values a personwants in an organization. For example, the wording of the original Orga-nizational Culture Profile (OCP; O’Reilly, Chatman, & Caldwell, 1991)requests that respondents indicate what values they prefer or want in an or-ganization, rather than what their personal values actually are. These scoresare then compared to what they receive from the organization. Worded insuch a way, studies using the original OCP assess value-fulfillment whichcorresponds more directly to needs–supplies complementary fit.

Studies of supplementary fit have traditionally used the broadest ar-ray of content dimensions. With roots in theories of interpersonal at-traction (Byrne, 1971) and the attraction–selection–attrition framework(Schneider, 1987), supplementary fit requires that P and E are at similarlevels on commensurate dimensions. These dimensions may include val-ues, goals, personality traits, or attitudes. For example, Cable and Judge(1996) shortened the OCP and revised it to be more clearly a measure of


supplementary fit, by rephrasing the questions to “What values are mostindicative of you? [emphasis added]” and “What values are most indicativeof the organization?” Personality and goals have also been used to assesssupplementary fit in the domains of PO, PG, and PS fit (e.g., Adkins et al.,1994; Kristof-Brown & Stevens, 2001; Ryan & Schmit, 1996; Witt & Nye,1992).

Reviews of fit often contain heated debates about the relative meritsof these dimensions and how they should be measured (e.g., Edwards,1994; Kristof, 1996; Meglino, Ravlin, & Adkins, 1991). This has beenparticularly true in the domain of PO fit. Chatman (1991) argued for val-ues as the basis of PO fit because values are enduring characteristics ofindividuals and organizations. Yet, Ryan and Kristof-Brown (2003) sug-gested that personality traits are more stable, proximal to behavior, andvisible in others’ behavior than are values. They conclude, therefore, that“personality-based PO fit should have at least as strong as, if not a stronger,influence on individuals’ attitudes and behaviors than would values-basedfit” (p. 266). An important consideration, however, is that although simi-larity on values is believed to be universally desirable (Meglino & Ravlin,1998), sometimes personality dissimilarity may be preferable (Carson,1969). For example, Kristof-Brown et al. (in press) demonstrate that peo-ple high on extraversion feel more attraction to their teams if the othermembers are more introverted, and vice versa. If similarity on some traits,but not others, is desirable, then overall personality congruence shouldhave weaker relationships with outcomes than does value congruence. Al-ternatively, for goals, PO congruence should always be preferable. Sharinggoals makes it more likely that individuals will receive support and rein-forcement for goal attainment. However, goals are less stable than values.Therefore, compared to other supplementary fit dimensions, we expect thatgoals will result in effect sizes greater than personality-based measures,but less than values-based measures. KSA-based studies of PO fit are rare,although some authors have included requisite KSAs into measures thatcombine multiple content dimensions to assess fit. For example, Bretzand Judge (1994) assessed fit along the dimensions of values, personality,needs, and KSAs. By including each of these dimensions, such combinedmeasures are likely to capture a more holistic assessment of fit. Therefore,it is expected that studies using combined measures will report strongereffect sizes than studies assessing just value congruence.

Measurement of Fit

Direct versus indirect measures. A potentially meaningful distinctionbetween types of fit studies is whether they assess fit directly or indirectly


(Kristof, 1996). Most scholars agree that perceived fit1 is defined by adirect assessment of compatibility (French et al., 1974; Kristof, 1996).Kristof (1996) distinguished this from actual fit, an inclusive term usedto describe measures in which researchers indirectly assess fit throughexplicit comparisons of separately rated P and E variables. French etal. (1974) further differentiated such explicit comparisons into subjec-tive fit, defined as the match between the person and environment as theyare perceived and reported by the person, and objective fit as the matchbetween the person as he or she really is and the environment as it ex-ists “independently” of the person’s perception of it (French et al., 1974,p. 316). Over the years, the terms perceived and subjective fit have oftenbeen used interchangeably (e.g., Cable & DeRue, 2002; Judge & Cable,1997; Kristof, 1996). However, because the cognitive processes underly-ing each may differ, we believe it is important to distinguish between thesetypes of fit. Thus, we use the terms as follows: (a) perceived fit, when anindividual makes a direct assessment of the compatibility between P andE; (b) subjective fit, when fit is assessed indirectly through the compari-son of P and E variables reported by the same person; and (c) objectivefit, when fit is calculated indirectly through the comparison of P and Evariables as reported by different sources.

When an individual has a high degree of contact with reality (i.e., as-sesses the environment accurately) and an accurate self-assessment, thesethree types of fit should have similar relationships with criteria (Frenchet al., 1974). In practice, however, they are often only weakly related(Cable & Judge, 1997; Kristof-Brown & Stevens, 2001). This may be dueto individuals’ propensity to interpret environmental cues in ways that al-low them to maintain a positive self-concept (Endler & Magnussen, 1976;French et al., 1974). Both self-perception theory (Bem, 1967) and cogni-tive dissonance theory (Festinger, 1957) suggest that individuals are drivento maintain internally consistent perceptions. It would produce cognitivedissonance for an individual to appraise a work environment as providinga poor fit but still report a high level of satisfaction with that environ-ment. Therefore, it is likely that perceived, subjective, and objective fitdiffer not only in how they are measured but also in what they representconceptually.

Perceived fit allows the greatest level of cognitive manipulation be-cause the assessment is all done in the head of the respondents, allowingthem to apply their own weighting scheme to various aspects of the envi-ronment. This permits individual differences in importance or salience of

1Note that we define perceived and subjective fit to be consistent with French et al.’s(1974) original use of the terms, but these labels are reversed in Verquer et al. (2003).


various dimensions to be captured in their ratings. Alternatively, indirectmeasures require separate ratings across specific P and E dimensions, andeach dimension is weighted equally. Indirect measures of subjective fitstill have P and E variables assessed by the same source, which does notfully negate consistency biases. Because both perceived and subjective fitare assessed by a single source, we expect that they will have similar ef-fects on outcomes, particularly attitudes, with perceived fit having slightlystronger effects because it allows a single individual to report a holisticassessment of fit and is more prone to consistency effects.

Compared to perceived and subjective fit, objective fit is a less prox-imal determinant of attitudes and behavior (Cable & DeRue, 2002). Yet,unless people are completely removed from reality, the objective environ-ment should have some influence on their perceptions of E. The objectiveenvironment may be made quite clear in experimental studies that use de-scriptions of the environment (e.g., Dineen, Ash, & Noe, 2002; Judge &Bretz, 1992; Kristof-Brown, Jansen, & Colbert, 2002). In addition, strongorganizational cultures and the shared perceptions of coworkers constitutesocially derived environments that have been found to have a meaningfulimpact on how people react to their work situations (e.g., Kozlowski &Klein, 2000; Salancik & Pfeffer, 1978). However, because this objectivereality must be filtered through individuals’ perceptions, we expect thatobjective fit will have the weakest relationships with most criteria. How-ever, this decrease should be less pronounced for performance criteriathan for attitudes because maximum performance should be limited by anobjective demand–ability mismatch.

Study Design

Assessing the environment. In cases of objective fit, Van Vianen(2000) implied another potential moderator when she differentiated be-tween the fit of a person with an organization and the fit of a personwith the people in the organization. Although Schneider’s (1987) ASAframework purports that “the people make the place,” group effects the-ory (e.g., Blau, 1960; Merton & Kitt, 1950) and theories of organizationalculture (e.g., Levinthal & March, 1993; Schein, 1992) suggest that higher-level units are more than the aggregate of attributes of their members.Thus, it may be overly simplistic to assume that the arithmetic mean ofthe people in a unit can capture the extent of culture or climate. Be-cause aggregate personal characteristics reflect current members only,whereas culture and climate reflect the totality of a unit’s history, we ex-pect weaker relationships between fit and criteria in studies using aggregatemembers’ personal characteristics, rather than perceptions of the overallunit.


Sampling strategy. Recent results have demonstrated that meta-analytic estimates are not stable when single-unit (e.g., job, group,organization) samples and multi-unit samples are combined (Gully,Incalcaterra, Joshi, & Beaubien, 2002; Ostroff & Harrison, 1999; Roth,Bevier, Bobko, Switzer, & Tyler, 2001). Although PG and PS studies ne-cessitate multi-unit samples to obtain adequate sample size, PJ and POstudies are often conducted for one job or within one organization. There-fore, single- versus multi-unit samples are a potential moderator for thesetypes of fit. Because multi-unit samples permit a broader range of environ-ments to be represented, there is reason to expect that fit relationships willbe stronger in multi-unit samples. However, Chatman (1989) noted that fitwill be most influential in strong situations. Therefore, when a single-unitenvironment is strong, relationships should be greater than in multi-unitsamples, but the reverse is true for single-unit samples with weak envi-ronments. Because of these competing rationales, we are unable to predictthe direction that this moderator will take.

Common method bias. Finally, it is important to address concernsthat have been raised regarding the heavy reliance on single-source report-ing of fit–outcome relationships (Edwards, 1993; Van Vianen, in press).Despite general awareness that common rater bias can inflate reportedeffect sizes, particularly for attitude–attitude relationships (Podsakoff,MacKenzie, Lee, & Podsakoff, 2003), PE fit studies often include predic-tor and outcome variables reported by the same person. This is particularlyproblematic for studies that examine the relationship between perceivedor subjective fit and attitudes. Because common rater bias can produceartifactual covariance due to consistency biases and illusory correlations(Podsakoff et al., 2003), we anticipate that the strongest effect sizes willbe reported for studies in which fit and the criteria are rated by the sameindividual, followed by those using a rater for two of the three potentialvariables (P, E, and criteria), and finally by those using separate ratings ofeverything.

One approach to reducing the impact of common rater bias, whichis primarily a concern in fit–attitude studies, is to temporally separatethe measurement of the predictor and criterion. Because a time delayshould reduce consistency and illusory correlations between constructs(Podsakoff et al., 2003), we expect larger effect sizes between fit andattitudes measured concurrently versus at different points in time.

Comparing Types of Fit

It is worth mentioning why we do not treat type of fit (PJ, PO, PG,PS) as a moderator. Because each type of fit represents compatibility withdifferent aspects of the environment, and has been shown to have unique


impacts on outcomes (Cable & DeRue, 2002; Kristof-Brown, 2000), weanalyze each separately. However, we do anticipate differences in the rela-tive magnitude of each type with various criteria. Research has shown thatjob-related constructs are most strongly associated with attitudes aboutthe job, whereas organization-related constructs are more closely relatedto organizational attitudes (Shore & Martin, 1989). In the domain of fit,job satisfaction should be most strongly associated with PJ fit, organiza-tional commitment with PO fit, satisfaction with coworkers with PG fit,and satisfaction with supervisor with PS fit. Other criteria, such as intentto quit, tenure, and turnover are likely to be influenced by multiple typesof fit because they represent attitudes or behaviors relevant to the totalwork experience (Barber, 1998; Mitchell, Holtom, Lee, Sablynski, & Erez,2001). The same is expected for organizational attraction because appli-cants generally get recruited based on elements of the job and organizationsimultaneously. However, intent to hire is expected to be more related toPJ than PO fit because the emphasis of most selection techniques is toassess whether the applicant has the requisite job-related skills (Bowen,Ledford, & Nathan, 1991).

Whereas task performance is highly dependent on skill-based job pro-ficiency, contextual performance has volition and predisposition as itsmajor source of variation and is not likely to be role-prescribed (Borman& Motowidlo, 1997). It is more directed toward helping and cooperatingwith others, supporting the organization’s mission, and putting in extraeffort when necessary. Therefore, PJ fit should have the greatest impacton task performance, whereas contextual performance is more likely tobe influenced by PO or PG fit. Because overall performance contains bothtask and contextual elements, all types of fit should be relevant.

Although we treat the various types of fit separately, it is informativeto examine the relationships between them. Kristof (1996) called for anincrease in research that simultaneously examines multiple types of fit,and recent studies show a trend in this direction (e.g., Cable & Derue,2002; Saks & Ashforth, 2002). Because of their emphasis on the people inthe environment, and on need–supplies and supplementary fit conceptual-izations, we expect that PO, PG, and PS fit will be more strongly related toeach other than to PJ fit. This should particularly be true for cases of PJ fitthat reflect a demands–abilities perspective, rather than a needs–suppliesperspective. Moreover, because relationships with coworkers and supervi-sors are involved in everyday work experiences, we anticipated that PS andPG fit would be more strongly related than either one with PO fit, whichmay be tempered by the closer relationships with colleagues. Finally, weexpect that all relationships between types of fit will be ameliorated whena direct versus indirect measurement strategy is used because of the in-creased likelihood of consistency effects in measures of perceived fit.


Polynomial Regression

One of the most dramatic changes to studies of PE fit in recent yearswas the introduction of polynomial regression as an alternative to ProfileSimilarity Indices (PSIs) for assessing fit indirectly. PSIs include differ-ence scores, a category of indices including (D, |D|, D1, D2, and Maha-lanobis’ D), base fit measures on the sum of differences between P and Eprofile elements (Edwards, 1993). They also include profile correlations,which assess similarity in the rank ordering of elements within P andE profiles (Edwards, 1993). Despite the prevalence of these approachesand their ability to capture the “holistic” nature of fit, they engenderedcriticism around issues of conceptual ambiguity, discarded information,insensitivity to the source of differences, and overly restrictive constraints(Cronbach, 1958; Edwards, 1993; Nunally, 1962).

In response to these concerns, Edwards (1993, 1994, 1996) suggestedusing polynomial regression to assess PE fit. Fundamentally, this approachavoids collapsing person and environment measures into a single score thatcaptures “fit.” Instead, both P and E, and associated higher-order terms(P2, P × E, and E2) are included as predictors. Because the P and E and thedependent variables are all unique, fit relationships are depicted in three-dimensional surface plots. Although effect sizes can be calculated fromthe multiple correlations of these studies, these include main and quadraticeffects not present in direct or PSI measures of fit, rendering a meta-analysis of their results ambiguous. To determine whether a fit relationshipis supported, characteristics of the surface plot must be examined. Resultsoften depict more complicated relationships than are determinable withPSIs (e.g., Edwards, 1993, 1996). Because of their increased usage, as wellas the potentially useful insights that they provide regarding the nature offit, we include a qualitative summary of their findings in our review.

Method

Data Collection

To identify studies for inclusion in the meta-analyses, we searchedseveral social science databases and national conference programs usingthe following key words: fit, congruence, similarity, interaction, person–environment, person–organization, person–job, person–culture, person–group, person–team, person–supervisor, and supervisor–subordinate.Specifically, we searched the American Psychological Association’sPsycINFO (1887–2003), ABI Inform (1971–2003), and DissertationAbstracts International (1861–2003) databases for relevant studies. Toobtain unpublished or in-press research we searched the Society for


Industrial and Organizational Psychology (1995–2004) and Academy ofManagement (1994–2004) annual conference programs using the searchterms. We also conducted a manual search of our files and reviewedthe reference lists of other summaries (Edwards, 1991; Kristof, 1996;Verquer et al., 2003) to identify relevant articles missed in the comput-erized search. Finally, we contacted 24 prominent fit researchers andasked them to share their emerging or unpublished research relatingto fit.

Using these search procedures, we identified over 750 total studies,which we then screened to determine their relevance. We excluded nonem-pirical studies (e.g., review articles, conceptual articles) and articles on top-ics previously described as outside the purview of this review. Additionalexclusions were studies conducted in nonwork contexts (e.g., Burnett,Vaughan, & Moody, 1997; Luke, Roberts, & Rappaport, 1993; O’Reilly& Chatman, 1986) and those examining fit between people and nationalcultures (e.g., Harrison, Shaffer, & Bhaskar, 2002; Van Vianen, De Pater,Kristof-Brown, & Johnson, 2004).

All work-related outcomes were considered for inclusion, but in gen-eral, only those having five or more total correlations or a total samplesize of over 1,000 are reported here. A few exceptions are made formoderator analyses or criteria that are conceptually relevant to PG andPS fit, which have a small number of total studies. Preentry criteria in-cluded organizational attraction and job acceptance reported by appli-cants, and intent to hire and job offers from the organization. Postentryoutcome variables included job satisfaction, organizational satisfaction,coworker satisfaction, cohesion, supervisor satisfaction, LMX, organi-zational commitment, organizational identification, intentions to quit,trust in manager, politics, task performance, contextual performance,overall performance, withdrawal behaviors, strain, turnover, and con-current tenure. Not all dependent variables were available for all typesof fit.

For studies that assessed perceived fit, the correlations of these directmeasures with the criteria were used to represent the effect size. For studiesassessing subjective or objective fit using a PSI, correlations of the calcu-lated fit term and the criteria were generally reported. In a small number ofstudies, an F-statistic or t-statistic was converted to a correlation (Cohen &Cohen, 1983). After evaluating the studies based on the inclusion criteria,172 codable studies, producing 836 unique effect sizes, were available forour meta-analyses. Sixty-two studies with 225 effect sizes were included inthe PJ fit meta-analyses; 110 studies with 450 effect sizes were included inthe PO fit meta-analyses; 20 studies with 104 effect sizes were includedin the PG fit meta-analyses; and 17 studies with 57 effect sizes were


included in the PS fit meta-analyses.2 Twenty-seven studies provided 34correlations between various types of fit. Twenty-five additional studiesreported polynomial regression results, which are qualitatively reviewed.All studies included in the meta-analyses are indicated in the referencesection, as are the polynomial regression studies.

Data Coding

We coded the characteristics of each study on multiple dimensions.Specifically, we coded the following: type of fit (PJ, PG, PO, and PS),context (preentry vs. postentry), sample size, effect size, and reliabilityof measures. We also coded for potential moderators. First, we coded theconceptualization of fit: supplementary, complementary needs–supplies,complementary demands–abilities or combined (any combination of theprevious three types). Second, we coded for the content dimension onwhich fit was assessed: values, personality, needs, goals, KSAs, combinedmeasures (i.e., including multiple content dimensions), or other (e.g., strat-egy). Third, we coded whether direct measures of perceived fit or indirectmeasures of objective or subjective fit were used. Within the subgroup ofstudies that used objective measures, we coded whether the environmentwas assessed as an independent entity or as the aggregation of members’characteristics. We coded all PJ and PO studies for whether the samplingstrategy was single unit or multi unit. Finally, to assess the impact of com-mon method bias we coded studies with performance criteria as using allcommon raters (ACR), partially common raters (PCR), or no commonrater (NCR), and studies with attitudinal criteria as collecting predictorsand criteria concurrently or separated in time.

Each article was coded independently by at least two of the authors.Coding discrepancies were resolved through discussion and by referringto the information provided in the original article and in the coding defini-tions. Given the ambiguities inherent in many of these studies, we foundthe use of multiple raters per article to be beneficial. After a prelimi-nary discussion of sample articles, average agreement calculated acrossall codes for a random subsample of 15% of the studies was 97%.

Meta-Analytic Procedures

We conducted the meta-analyses using the formulas from Hunterand Schmidt (2004), with the aid of the meta-analytic software program

2If fit was assessed on multiple facets of a content dimension (e.g., fit on four separatevalues), the correlations were averaged to create one effect size.


developed by Schmidt and Le (2004). The means and variances of the meta-analytic estimates were corrected for artifactual variance due to samplingerror and attenuation due to measurement error in the predictor and thecriterion. Because the purpose of this study was to provide a meaningfulestimate of the relationship between fit and various criteria, correcting forattenuation in both the predictor and the criterion was necessary. Relia-bilities for the predictor and criterion were not available in each study;therefore, we used artifact distributions to correct for measurement errorin both (Hunter & Schmidt, 2004). This was done by utilizing the in-formation contained in all studies that did report reliability estimates foreither the predictor or criteria variables in question. A single distributionof reliabilities was then created for each variable based on these estimates,and this distribution was used to correct the appropriate variable for un-reliability. Note that in order to best estimate the artifact distribution fora given variable, the same distribution was used for that variable acrossall meta-analyses. This was done to better reflect the true distribution ofreliabilities in the population, rather than just in the subset of studies fora given meta-analysis.

Because reliabilities are not available for objective criteria (tenure,turnover, withdrawal, job offer, and job acceptance), no correction formeasurement error was made for these variables. In addition, no correctionwas made for dichotomization in turnover because almost none of thestudies provided turnover rates, and this type of correction is not withoutcontroversy (Hunter & Schmidt, 2004; Williams, 1990). For studies thatused a difference score to calculate fit, the reliability of the differencescore was calculated when enough information was provided in the study(i.e., the reliability of the person scale and the environment scale, and thecorrelation between the two scales; Hunter & Cohen, 1974). Finally, thepopulation of interest and the population examined in each study were thesame and therefore no correction for range restriction was needed. Thiswas true even when fit was examined in a preentry setting, as the criteriaof interest in those settings were either job acceptance or job offer.

It is important to note that several of the studies measured fit in multipleways for the same sample and criteria. To ensure that the assumption ofindependent samples was not violated (Hunter & Schmidt, 2004), no morethan one effect size from a study was included in each meta-analysis. Ifthere were multiple effect sizes from a single study (e.g., correlationsfrom a perceived PJ measure and a subjective PJ fit measure with jobsatisfaction), then the average of these two unique effect sizes was usedin the analysis of the overall relationship (i.e., PJ fit – job satisfaction).However, when specific effect sizes pertaining to the various moderatorswere available, these, rather than the aggregate correlations, were used inthe moderator analyses.


Moderator analyses were conducted to test the theoretically derivedpropositions articulated earlier, when there was a large enough k and Nto merit discussion. Given the small number of studies on PG and PSfit with the same criteria, no moderator analyses were possible for thesetypes of fit. A moderator would be indicated when two conditions aremet. First, there would be a large difference in the effect sizes betweenthe compared groups of studies. Second, the average variance in effectsizes within each of these groups should decrease (Hunter & Schmidt,2004). The 80% credibility intervals reported in each meta-analysis tableindicate the generalizability of the relationship across situations (Hunter& Schmidt, 2004). If the credibility interval does not include zero, therelationship is said to be generalizable. If the interval does not includezero, yet there is still considerable variance unaccounted for, the direc-tion of the relationship may still generalize (i.e., still be positive or nega-tive across situations), but its strength may vary depending on additionalmoderators.

Results

Tables 1–4 present the meta-analyses for the four types of fit, the crite-ria, and the moderator analyses. The first column indicates the fit–criterionrelationship examined, and any relevant subsamples due to moderators.The second and third columns provide the number of effect sizes (k) andtotal sample size (N), respectively, included in the meta-analytic estimate.In the fourth column, the sample size weighted mean observed correlationis presented. The lower and upper bounds of the 95% confidence inter-val of the observed correlations are shown in the fifth and sixth columns,respectively. The seventh column contains the estimated true score corre-lation (ρ), followed by the standard deviation of this estimate in the eighthcolumn (SD ρ). The lower and upper bound of the 80% credibility inter-val are presented in the ninth and tenth columns, respectively. Finally, thepercentage of variance accounted for by the artifacts is contained in thelast column. Table 5 reports the meta-analytic results of the interrelation-ships between types of fit, as well as moderator analyses for direct versusindirect measures.

Person–Job Fit

As shown in Table 1, the first set of meta-analyses includes the cor-relations between PJ fit and outcomes. PJ fit has strong correlations withthe three primary attitudes studied in the fit literature: .56 with job sat-isfaction, .47 with organizational commitment, and −.46 with intent toquit. PJ fit has a moderate relationship with the attitudes of coworker


TAB

LE

1M

eta-

Ana

lysi

sR

esul

tsof

Pers

on–J

obF

it

95%

CI

80%

CV

kN

Avg

rL

ower

Upp

erρ

SDρ

Low

erU

pper

%va

rex

plai

ned

Job

satis

fact

ion

4712

,960

.44

.20

.68

.56

.138

.38

.73

22.2

1C

ompl

emen

tary

:D–A

123,

735

.32

.14

.50

.41

.085

.30

.52

40.8

7C

ompl

emen

tary

:N–S

328,

726

.48

.19

.77

.61

.172

.39

.83

15.5

6C

ombi

ned

[D–A

&N

–S]

61,

036

.49

.20

.78

.62

.159

.41

.82

22.6

5D

irec

t:Pe

rcei

ved

237,

205

.45

.23

.67

.58

.112

.43

.72

28.3

4In

dire

ct24

4,90

8.4

4.1

3.7

5.5

6.1

86.3

2.7

916

.42

Subj

ectiv

e18

4,37

7.4

6.1

9.7

3.5

9.1

63.3

8.8

018

.13

Obj

ectiv

e6

531

.22

−.05

.49

.28

.118

.13

.43

54.6

7Si

ngle

job

102,

546

.47

.20

.74

.59

.154

.39

.79

19.4

7M

ultip

lejo

bs37

10,4

14.4

3.1

9.6

7.5

5.1

32.3

8.7

223

.45

Sam

etim

e36

9,70

4.4

4.1

9.6

9.5

6.1

41.3

8.7

421

.67

Dif

fere

nttim

es10

2,74

2.4

5.2

0.7

0.5

7.1

42.3

9.7

621

.29

Org

aniz

atio

nalc

omm

itmen

t18

4,07

3.3

9.1

0.6

8.4

7.1

70.2

6.6

916

.76

Com

plem

enta

ry:D

–A4

873

.25

.01

.49

.31

.120

.15

.46

30.6

9C

ompl

emen

tary

:N–S

91,

833

.31

.06

.56

.37

.139

.20

.55

25.7

3C

ombi

ned

[D–A

&N

–S]

586

1.4

2.0

5.7

9.5

1.2

15.2

4.7

913

.32

Dir

ect:

Perc

eive

d11

2,15

4.3

6.0

3.6

9.4

4.1

87.2

1.6

816

.05

Indi

rect

82,

011

.40

.13

.67

.50

.156

.30

.69

18.0

0Su

bjec

tive

321

6.1

7.0

1.3

3.2

10

.21

.21

100.

00O

bjec

tive

51,

795

.43

.19

.67

.53

.132

.36

.70

19.1

5Si

ngle

job

71,

987

.43

.19

.67

.53

.134

.36

.70

21.2

8M

ultip

lejo

bs11

2,08

6.3

5.0

2.6

8.4

3.1

86.1

9.6

616

.71

Sam

etim

e10

2,43

8.4

4.1

9.6

9.5

4.1

40.3

6.7

221

.46

Dif

fere

nttim

es8

1,63

5.3

1.0

2.6

0.3

8.1

67.1

7.5

919

.25


Inte

ntto

quit

163,

849

−.37

−.59

−.15

−.46

.118

−.61

−.30

29.0

9C

ompl

emen

tary

:D–A

698

2−.

18−.

42.0

6−.

23.1

07−.

36−.

0943

.69

Com

plem

enta

ry:N

–S8

2,81

6−.

40−.

64−.

16−.

50.1

26−.

66−.

3420

.98

Com

bine

d[D

–A&

N–S

]5

783

−.46

−.75

−.17

−.57

.167

−.78

−.35

21.4

5D

irec

t:Pe

rcei

ved

112,

065

−.40

−.65

−.15

−.49

.132

−.66

−.32

28.2

5In

dire

ct6

1,86

6−.

33−.

57−.

09−.

40.1

34−.

57−.

2320

.74

Subj

ectiv

e3

267

−.15

−.50

.20

−.18

.182

−.41

.05

33.4

6O

bjec

tive

31,

599

−.36

−.52

−.20

−.44

.076

−.54

−.34

34.9

2Si

ngle

job

41,

770

−.35

−.51

−.19

−.43

.077

−.53

−.33

37.2

7M

ultip

lejo

bs12

2,05

9−.

39−.

64−.

14−.

48.1

40−.

66−.

3027

.63

Sam

etim

e7

2,38

7−.

38−.

50−.

26−.

47.0

44−.

52−.

4169

.26

Dif

fere

nttim

es8

1,13

1−.

38−.

73−.

03−.

47.1

95−.

72−.

2218

.99

Cow

orke

rsa

tisfa

ctio

n6

2,28

0.2

5.1

3.3

7.3

2.0

39.2

7.3

775

.98

Supe

rvis

orsa

tisfa

ctio

n4

1,98

7.2

8.1

2.4

4.3

3.0

78.2

3.4

332

.46

Org

aniz

atio

nali

dent

ifica

tion

81,

619

.29

.11

.47

.36

.063

.28

.44

65.8

3O

vera

llpe

rfor

man

ce19

1,93

8.1

6−.

19.5

1.2

0.1

86−.

04.4

429

.95

Com

plem

enta

ry:D

–A8

547

.10

−.33

.53

.12

.228

−.17

.41

30.6

3C

ompl

emen

tary

:N–S

81,

558

.16

.00

.32

.20

.041

.14

.25

81.9

2D

irec

t:Pe

rcei

ved

355

2.1

8−.

25.6

1.2

2.2

49−.

10.5

411

.47

Indi

rect

161,

386

.16

−.17

.49

.19

.153

.00

.39

42.7

8Su

bjec

tive

1029

5.1

3−.

48.7

4.1

6.3

04−.

23.5

536

.17

Obj

ectiv

e6

1,09

1.1

7−.

01.3

5.2

0.0

64.1

2.2

966

.59

Sing

leJo

b8

801

.08

−.17

.33

.10

.100

−.03

.23

60.3

0M

ultip

leJo

bs15

1,44

1.2

2−.

11.5

5.2

7.1

72.0

5.4

932

.25

Stra

in10

3,50

5−.

23−.

39−.

07−.

28.0

78−.

38−.

1840

.82

Com

plem

enta

ry:D

–A4

2,02

5−.

25−.

43−.

07−.

30.0

91−.

42−.

1926

.64

Com

plem

enta

ry:N

–S6

1,64

2−.

25−.

43−.

07−.

31.0

81−.

41−.

2043

.98

Tenu

re5

1,48

4.1

6−.

08.4

0.1

8.1

15.0

3.3

223

.42

Tur

nove

r8

1,49

6−.

07−.

25.1

1−.

08.0

59−.

16−.

0164

.95

Org

aniz

atio

nala

ttrac

tion

48,

131

.40

.09

.71

.48

.183

.25

.72

4.46

Inte

ntto

hire

41,

132

.59

.30

.88

.67

.156

.47

.87

12.6

7

D–A

=de

man

ds–a

bilit

ies

fit.

N–S

=ne

eds–

supp

lies

fit.


TAB

LE

2M

eta-

Ana

lysi

sR

esul

tsof

Pers

on–O

rgan

izat

ion

Fit

95%

CI

80%

CV

kN

Avg

rL

ower

Upp

erρ

SDρ

Low

erU

pper

%va

rex

plai

ned

Job

satis

fact

ion

6542

,922

.35

.08

.62

.44

.167

.23

.65

9.42

w/o

Van

couv

eran

dSc

hmitt

(199

1)64

29,5

34.4

0.1

3.6

7.5

0.1

68.2

9.7

211

.84

Supp

lem

enta

ry41

33,7

67.3

4.0

7.6

1.4

3.1

67.2

2.6

48.

27w

/oV

anco

uver

and

Schm

itt(1

991)

4020

,379

.41

.14

.68

.52

.169

.30

.73

11.1

8C

ompl

emen

tary

:N–S

309,

284

.37

.06

.68

.46

.186

.22

.70

12.3

8V

alue

s45

23,2

11.4

1.1

6.6

6.5

1.1

57.3

1.7

112

.62

Pers

onal

ity5

548

.07

.01

.13

.08

0.0

8.0

810

0.00

Goa

ls3

14,3

67.2

4.2

0.2

8.3

10

.31

.31

100.

00M

ultid

imen

sion

al10

3,52

4.4

4.1

9.6

9.5

5.1

52.3

5.7

416

.35

Dir

ect:

Perc

eive

d30

19,5

34.4

5.2

3.6

7.5

6.1

24.4

1.7

217

.70

Indi

rect

:39

24,3

39.2

8.0

4.5

2.3

5.1

42.1

7.5

312

.12

w/o

Van

couv

eran

dSc

hmitt

(199

1)38

10,9

51.3

2−.

01.6

5.4

0.1

97.1

5.6

511

.65

Subj

ectiv

e24

8,18

7.3

7.0

4.7

0.4

6.1

98.2

1.7

110

.50

Obj

ectiv

e19

16,7

06.2

3.1

1.3

5.2

9.0

56.2

2.3

639

.33

w/o

Van

couv

eran

dSc

hmitt

(199

1)18

3,31

8.1

9−.

05.4

3.2

4.1

21.0

8.3

934

.69

Rat

ings

ofor

gani

zatio

nas

E13

1,78

0.1

7−.

10.4

4.2

2.1

32.0

5.3

938

.32

Agg

rega

tem

embe

rpr

ofile

sas

E6

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Schm

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(199

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304 PERSONNEL PSYCHOLOGYTA

BL

E2

(con

tinue

d)

95%

CI

80%

CV

kN

Avg

rL

ower

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erρ

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Low

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2429

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172,

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51,

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ter

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91,

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draw

al(A

bsen

teei

sm)

71,

370

−.05

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rain

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irec

t:Pe

rcei

ved

62,

184

−.28

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dire

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1−.

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rgan

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iona

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actio

n11

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ect:

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rect

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525

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0.2

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210

0.00

Job

acce

ptan

ce4

1,82

9.2

2.0

4.4

0.2

4.0

89.1

3.3

624

.91

Inte

ntto

hire

92,

518

.53

.31

.75

.61

.116

.47

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23.8

9D

irec

t:Pe

rcei

ved

82,

402

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.70

.146

.52

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16.0

1In

dire

ct3

562

.16

−.13

.45

.18

.153

−.02

.38

22.5

8Jo

bof

fer

81,

556

.29

−.14

.72

.32

.235

.02

.63

9.16

Dir

ect:

Perc

eive

d6

1,17

8.4

5.1

6.7

4.5

0.1

47.3

1.6

918

.29

Indi

rect

691

8.0

3−.

19.2

5.0

3.0

75−.

06.1

358

.33


TABLE 3Meta-Analysis Results of Person–Group Fit

95% CI 80% CV% var

k N Avg r Lower Upper ρ SDρ Lower Upper explained

Job satisfaction 9 1822 .24 .12 .36 .31 0 .31 .31 100.00Organizational 12 2306 .15 −.05 .35 .19 .084 .08 .30 52.28

commitmentIntent to quit 6 943 −.17 −.33 −.01 −.22 .028 −.25 −.18 92.31Coworker 5 1210 .32 .16 .48 .42 .057 .34 .49 70.16

satisfactionSupervisor 4 1545 .23 .05 .41 .28 .091 .16 .39 31.28

satisfactionOverall 9 1946 .15 .03 .27 .19 0 .19 .19 100.00

performanceContextual 4 1015 .18 .00 .36 .23 .089 .12 .35 44.72

performancePolitical 5 979 −.13 −.23 −.03 −.17 0 −.17 −.17 100.00

perceptionsTenure 10 1429 .05 −.13 .23 .06 .032 .02 .10 89.85Group 4 889 .22 −.03 .47 .27 .140 .09 .45 24.60

cohesion

satisfaction (.32), supervisor satisfaction (.33), and organizational identi-fication (.36). It has a modest correlation with overall performance (.20)and is correlated somewhat more strongly (−.28) with indicators of strain.Among the criteria of organizational longevity, tenure, and turnover, PJfit had correlations of .18 and −.08, respectively. In the preentry con-text, PJ fit had strong correlations of .48 with organizational attractionand .67 with an organization’s intent to hire. Almost all of the 80%credibility intervals for PJ fit did not include zero, with the exceptionof PJ fit and overall job performance (80% CV: −.04 −.44). There-fore, the generalizability of this relationship is less definite than theothers.

Based on the number of studies of each criterion available for moder-ator analysis, four moderators could be reliably examined. The first wasthe conceptualization of fit. Only five studies included a supplementaryconceptualization of PJ fit, and no more than three of these addressedthe same criterion. Therefore, a reliable comparison of supplementaryand complementary conceptualizations could not be conducted for PJ fit.Within the complementary category, however, separating the studies bydemands–abilities, needs–supplies, or a combination of the two concep-tualizations explained more of the variance in the correlations in fourof the five cases where there were enough studies to perform moderator


TAB

LE

4M

eta-

Ana

lysi

sR

esul

tsof

Pers

on–S

uper

viso

rF

it

95%

CI

80%

CV

kN

Avg

rL

ower

Upp

erρ

SDρ

Low

erU

pper

%va

rex

plai

ned

Job

satis

fact

ion

51,

199

.35

.11

.59

.44

.112

.30

.59

30.0

6O

rgan

izat

iona

lcom

mitm

ent

71,

346

.07

−.15

.29

.09

.100

−.04

.21

42.5

4Pe

rcep

tions

ofpo

litic

s5

853

−.09

−.21

.03

−.12

0−.

12−.

1210

0.00

Supe

rvis

orsa

tisfa

ctio

n5

918

.39

−.02

.80

.46

.228

.17

.75

9.94

Lea

der–

mem

ber

exch

ange

362

8.3

7.1

9.5

5.4

3.0

76.3

4.5

347

.35

Tenu

re6

1,19

1.0

8−.

10.2

6.0

9.0

62.0

1.1

760

.43

Ove

rall

Perf

orm

ance

143,

461

.15

−.12

.42

.18

.153

−.01

.38

19.8

9D

irec

t:Pe

rcei

ved

81,

179

.25

.01

.49

.31

.114

.16

.45

41.3

6In

dire

ct7

2,30

7.0

9−.

09.2

7.1

1.0

94−.

01.2

333

.12


TAB

LE

5M

eta-

Ana

lysi

sR

esul

tsof

the

Inte

rrel

atio

nshi

psB

etw

een

Type

sof

Fit

95%

CI

80%

CV

kN

Avg

rL

ower

Upp

erρ

SDρ

Low

erU

pper

%va

rex

plai

ned

PO–P

J16

10,2

39.5

8.3

8.7

8.7

2.1

06.5

8.8

530

.05

Dir

ect:

Perc

eive

d14

9,99

7.5

9.4

3.7

5.7

4.0

71.6

5.8

349

.53

PJ:C

ompl

emen

tary

:D–A

71,

303

.37

.02

.72

.45

.200

.20

.71

16.0

8PJ

:Com

plem

enta

ry:N

–S6

2,28

2.5

9.4

7.7

1.7

3.0

25.7

0.7

689

.87

Indi

rect

:Obj

ectiv

e4

440

.19

−.12

.50

.24

.154

.04

.43

35.7

0PO

–PG

81,

607

.43

.10

.76

.54

.198

.28

.79

16.0

7D

irec

t:Pe

rcei

ved

51,

321

.39

.25

.53

.48

.050

.42

.55

70.9

3In

dire

ct:O

bjec

tive

551

2.4

8−.

131.

00.6

0.3

71.1

21.

008.

02PO

–PS

497

6.3

8.0

3.7

3.4

6.2

06.2

0.7

211

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analyses.3 It should be noted that for PJ fit, conceptualization (demands–abilities vs. needs–supplies) and content dimensions (KSAs vs. needs) arevirtually identical. Therefore, the analyses are relevant to both types ofmoderators.

Conceptualization of fit (or content dimension) acted as a modera-tor for job satisfaction, organizational commitment, intent to quit, andoverall performance but not for indicators of strain. As expected, the com-bined measure of PJ fit had a stronger correlation with attitudinal criteria,followed by needs–supplies fit, and then demands–abilities fit. The dif-ferences in the relationships for each were job satisfaction (combined:.62; needs–supplies: .61, demands–abilities: .41), organizational commit-ment (.51, .37, .31), and intent to quit (−.57, −.50, −.23). For overallperformance only needs–supplies and demands–abilities conceptualiza-tions could be compared, with the former being the stronger predictor(.20, .12). Although this does not support our prediction that demands–abilities fit would have a greater impact on performance, the effect sizesare more similar than for the attitudinal criteria. In contrast to the re-sults before the moderator analysis, the 80% credibility interval for the PJneeds–supplies—overall performance relationship did not include zero,supporting the generalizability of this particular relationship.

The second moderator examined was measurement strategy—directmeasures of perceived fit, indirect measures of subjective and objectivefit. Due to sample size constraints, the moderator analysis was conductedonly for the criteria of job satisfaction, organizational commitment, intentto quit, and overall performance. Like fit conceptualization, this analysisexplained more of the variance among the PJ fit correlations. We expectedthat the effects would be largest for direct measures of perceived fit, fol-lowed by indirect measures of subjective fit, and then objective fit. Thisprediction was supported for intent to quit (perceived: −.49, subjective:−.44, objective: −.18) and overall performance (.22; .20; .16). Althoughthe small difference in effect sizes across these measurement strategiesfor the performance criterion is notable, partial support for our predictionwas found for job satisfaction, in which direct perceived (.58) and indirectsubjective (.59) effects were virtually identical, with indirect objective fit adistant third (.28), and for organizational commitment in which objectivefit was lowest (.21), but subjective fit (.53) was greater than perceived fit(.44).

3Because studies using a demands–abilities conceptualization of fit used KSAs as theexclusive content dimension, and those investigating needs–supplies conceptualizationsused predominantly needs, we did not conduct a separate moderator analysis for contentdimensions.


The third moderator we examined was whether the sample containedonly one type of job (single-unit) or multiple jobs (multi-unit). We did notmake a prediction about the direction of this moderator, and the resultswere mixed. The single-job studies produced higher correlations for jobsatisfaction (single: .59, multiple: .55) and organizational commitment(single: .53, multiple: .43); whereas the multiple-job studies had largereffect sizes for intent to quit (single: −.43, multiple: −.48) and overallperformance (single: .10, multiple: .27).

Fourth, to examine the impact of common method bias on fit–attituderelationships, we examined whether timing of the predictor and criterionmeasures as concurrent versus temporally separate moderated effect sizes.We expected that the effect sizes would be greater when fit and attitudeswere measured at the same point in time, versus when the measures weretemporally separated. However, for both job satisfaction (same time: .56,different times: .57) and intent to quit (same time: −.47, different times:−.47), we found little to no difference in the corrected correlations. Onlyfor organizational commitment did the timing of the measures act as ex-pected (same time: .54, different times: .38).

Studies examining the PJ fit using a polynomial regression analysisproduced effect sizes generally in the range of the meta-analytic esti-mates in Table 1. However, visual inspection of the surface plots often re-vealed more complicated relationships (Edwards, 1993, 1994; Edwards &Harrison, 1993; Edwards & Rothbard, 1999; Livingston, Nelson, & Barr,1997; Shaw & Gupta, 2004). Rather, they suggest that fit at high levels ofP and E is generally better than fit at low levels and that main effects ofE often outweigh those of P, making inadequate supplies (P > E) moredetrimental than excess supplies (P < E) for attitudes.

Person–Organization Fit

Table 2 presents the correlations from the meta-analyses of PO fitand outcomes. PO fit had strong correlations with job satisfaction (.44)and organizational commitment (.51), and more moderate for intent toquit (−.35). Although the sample sizes for these analyses were large(N = 42,922 for job satisfaction, N = 36,093 for organizational com-mitment, N = 34,276 for intent to quit), one study may have overly influ-enced these results. To investigate the impact of Vancouver and Schmitt4

(1991; n = 13,388), analyses were conducted both with and without thisstudy. Without Vancouver and Schmitt (1991), the relationships between

4Because Vancouver, Millsap, and Peters (1994) used the same data set as Vancouverand Schmitt (1991), we only included the earlier study in our analysis.


PO fit and all three criteria increased (job satisfaction, .50; organizationalcommitment, .65; intent to quit, −.47).

The relationships between PO fit and most other attitudes were mod-erate: .39 with coworker satisfaction, .33 with supervisor satisfaction, .43with the employees’ trust in their managers. The correlation with orga-nizational satisfaction was substantially higher, .65. With measures ofperformance, PO fit had low correlations with overall job performance(.07) and task performance (.13), but moderate correlations with contex-tual performance (.27). PO fit also had weak relationships with tenure(.03), turnover (−.14), and organizational withdrawal (−.05), but moremoderate relationships with indicators of strain (−.27). With preentry cri-teria, PO fit had a correlation of .46 with organizational attraction, .24 withapplicant job acceptance, .61 with intent to hire, and .32 with job offers.Most of the 80% credibility intervals for PO fit did not include zero, withthe exceptions of overall performance, tenure, and withdrawal.

To test for moderators, we first examined the conceptualization offit (supplementary vs. complementary needs–supplies). Only Hutcheson(1999) examined the demands–abilities PO fit, so that category was notincluded in the moderator analyses. In contrast to our prediction thatneeds–supplies fit effects would be stronger than supplementary fit, thecorrelations with organizational commitment for supplementary fit werehigher, .53 (.74 without Vancouver & Schmitt, 1991) than needs–suppliesfit (.39), as was the case with intent to quit, −.36 (−.51); −.34. Forjob satisfaction supplementary fit was correlated slightly lower (.43) thanneeds–supplies fit (.46); however, this pattern reversed when Vancouverand Schmitt (1991) was excluded (supplementary: .52).

The second moderator examined was the content dimensions on whichfit was assessed. The majority of the studies examined values exclusivelyor used a combined measure that included multiple content dimensions.As predicted, multidimensional measures had stronger relationships thanvalues-only measures with job satisfaction (multidimensional: .55, values:.51), and intent to quit (multidimension: −.48, values: −.46), althoughthese differences were small. Alternatively, for organization commitment,values-based fit yielded a stronger relationship than multidimensionalmeasures (.68 and .59). Only for job satisfaction were we able to reli-ably compare these effects with personality-based and goal-based PO fit.Consistent with our prediction, goal-based PO fit had a weaker effect (.31)than value-based fit (.51) but stronger than personality-based fit (.08).

The third moderator examined was measurement strategy. Results weregenerally consistent with our prediction that direct measures of perceivedfit would have the strongest relationship with criteria, followed by in-direct subjective, and then indirect objective measures: job satisfaction(direct perceived, .56; indirect subjective, .46; indirect objective: .29),


organizational commitment (.77, .44, .27), and intent to quit (−.52, −.32,−.19). Only for the tenure criterion did the direction of the effects differ(.03, .01, .06). For all other criteria it was only possible to compare directperceived measures with studies using indirect measures (i.e., both sub-jective and objective fit studies are included). In each of these cases, directmeasures had stronger effects than did indirect measures: coworker satis-faction (direct perceived: .43, indirect: .19), supervisor satisfaction (.38,.14), overall performance (.12, .03), task performance (.22, .05), contextualperformance (.32, .20), turnover (−.16, −.12), and strain (−.34, −.17).Differences in the effect sizes were particularly large in the preentry con-text: organizational attraction (.62, .22), intent to hire (.70, .18), and joboffer (.50, .03). Excluding Vancouver and Schmitt (1991) did not changethe measurement strategy moderator results substantially.

The fourth moderator analysis examined whether it made a differenceif the objective environment was measured as the aggregate of employees’personal characteristics or their perceptions of the organizational cultureor climate. Due to sample size limitations, this analysis could only beconducted for job satisfaction and organizational commitment. In contrastto our prediction, for both criteria the effect size was slightly larger whenthe environment was an aggregation of personal characteristics (job satis-faction: .30, organizational commitment: .28) than when their aggregateof ratings of the organization were used (.22, .16). These results remainedmostly stable when Vancouver and Schmitt (1991) was excluded.

The fifth moderator analysis focused on whether the sampling strategycreated differences in the size of the relationships. We made no predictionsabout the direction of this moderator. For two of the three analyses, theeffect sizes for the single organization studies were higher than the multi-ple organization studies: organizational commitment (single organization:.63, multiple organizations: .49) and intent to quit (−.46, −.34). For thecriterion of job satisfaction the effect sizes were comparable for single(.42) and multiorganization studies (.44). However, when Vancouver andSchmitt (1991) was excluded, the effect sizes for single and multiple or-ganization studies became virtually identical.

We examined the PO fit data in two ways to determine if commonmethod bias was influencing effect sizes. First, we assessed whether thetiming (concurrent vs. separate) of predictor and criterion measures actedas a moderator for the attitudes of job satisfaction, organizational com-mitment, and intent to quit. For job satisfaction (same time: .45, differ-ent times: .39) and organizational commitment (same time: .51, differenttimes: .43), the expected smaller effect size for measures taken at differentpoints in time was obtained. However, this did not hold for intent to quit(same time: −.35, different times: −.47). Without Vancouver and Schmittthe effects were comparable (same time w/o Vancouver & Schmitt: −.46).Second, for the performance criteria, we examined whether common rater


bias influenced the reported effect sizes.5 We predicted that the effectswould be largest when fit and performance were reported by a commonrater (ACR), followed by partially common raters (PCR), and then by nocommon raters (NCR). Effects were consistent with this prediction fortask (ACR: .18, PCR: .14, NCR: .04) and contextual (ACR: .45, PCR:.27, NCR: .06) performance. However, for overall performance the mag-nitude of the last two relationships was reversed (ACR: .22, PCR: −.02,NCR: .18).

Studies of PO fit using polynomial regression generally producedlarger effect sizes than the meta-analytic estimates for other types of mea-sures (e.g., Cable & Edwards, 2004; Finegan, 2000; Kalliath, Bluedorn,& Strube, 1999; Lutrick, 2003; Van Vianen, 2000). However, examiningthe surface graphs in these studies suggested relationships that generallydiverged from traditional notions of symmetrical fit. Instead, these studiesdemonstrate that attitudes are most positive when both P and E are highversus when they are both low. In addition, most suggest nonsymmetricaleffects of misfit, such that excess E conditions have little negative impacton attitudes, whereas excess P conditions accompany dramatic decreasesin attitudes.

Person–Group Fit

The third set of meta-analyses provides estimates of the relationshipbetween PG fit and the criteria. As can been seen in Table 3, PG fit has amoderate true score correlation with the three most commonly studied cri-teria, job satisfaction, organizational commitment, and intent to quit. Thetrue score correlations with those criteria are .31, .19, and −.22, respec-tively. For the two most commonly assessed subfacets of job satisfaction,PG fit correlates .42 with coworker satisfaction and .28 with supervisorsatisfaction. PG fit also has a strong relationship (.47) with group cohesion.The correlation with overall performance is .19, whereas the correlationwith contextual performance is slightly higher at .23. PG fit has a moderatenegative relationship with how employees view the political environmentof their organization (−.17). The correlation with tenure is the weakest ofall of the criteria, at .06.

For PG fit, none of the 80% credibility intervals include zero, indi-cating the broad generalizability of PG fit across situations. Although the

5We did not conduct the common rater analysis for the attitudinal criteria because theresults would be isomorphic with the comparison between perceived and subjective fit(both ACR with attitudes as the criteria) and objective fit (PCR), which have already beendiscussed. All studies examining objective criteria (turnover, tenure, job offer, withdrawal,and job acceptance) were also excluded, because the results would be identical to thecomparison of subjective fit (PCR) with objective criteria with perceived and objective fit(NCR).


theoretical justifications for moderators discussed previously would applyto PG fit studies, because of the small number of studies in this categorya reliable moderator analysis was not possible.

Only a small number of studies have used a polynomial regression ap-proach to examine PG fit (e.g., Kristof-Brown & Stevens, 2001; Slocombe& Bluedorn, 1999; Strauss et al., 2001). As with other polynomial regres-sion studies, the multiple correlations are generally larger than for othertypes of fit measures. When these relationships are graphed, the resultspresent mixed support for fit relationships. Symmetrical, supplementaryfit relationships were supported for performance goals (Kristof-Brown &Stevens, 2001), polychronicity (Slocombe & Bluedorn, 1999) and hur-riedness (Jansen & Kristof-Brown, in press), and a symmetrical, comple-mentary fit relationship for extraversion and attraction to team members(Kristof-Brown, Barrick, & Stevens, in press). However, in most of thesestudies there were also results demonstrating that excesses of E are gen-erally not as detrimental as excesses of P and that fit at high levels (i.e.,high P and high E) is generally better than fit at low levels (i.e., low P andlow E).

Person–Supervisor Fit

The meta-analysis results for PS fit are presented in Table 4. All ofthe 80% credibility intervals did not include zero with the exceptions oforganizational commitment and overall performance. PS fit had a strongerrelationship with job satisfaction (.44) than organizational commitment(.09). The relationship with political perceptions was −.12. The relation-ships with supervisor-focused criteria were strong, with a correlation of.46 with supervisor satisfaction and .43 with LMX. PS fit had a correlationof .09 with tenure and .18 with overall performance. However, the relation-ship with overall performance was moderated by the conceptualization offit. Consistent with the predictions, the direct measures of perceived PSfit had a stronger relationship with performance (.31) than did indirectmeasures of subjective and objective fit (.11).

Only a handful of studies have taken a polynomial regression approachto PS fit (Antonioni & Park, 2001; Smith, 2002; Van Vianen, 2000). Thesestudies generally show little support for supplementary fit on personalityor values being related to employee attitudes or contextual performance,and the effects differ dramatically for various personality traits.

Comparing Types of Fit

Consistent with our predictions, attitudes referencing particular aspectsof the environment were most strongly associated with correspondingtypes of fit. Job satisfaction was more strongly related to PJ fit (.56) than


to PO (.44), PG (.31), or PS fit (.44). Organizational commitment was moststrongly related to PO fit (.51), followed closely by PJ fit (.47), and thenweakly related to PG fit (.19) and PS fit (.09). Alternatively, satisfactionwith coworkers was highest for PG fit (.42) compared to PJ (.32) and PO fit(.39), and satisfaction with supervisor was highest for PS fit (.46) comparedto PJ (.33), PO (.33), and PG fit (.28). Also consistent with expectations,organizational attraction was influenced comparably by PJ fit (.48) andPO fit (.46), and intent to hire was somewhat more strongly related to PJfit (.67) than PO fit (.61). In contrast to our predictions, intent to quit,tenure, and turnover were differentially influenced by various types of fit:intent to quit (PJ: −.46, PO: −.35, PG: −.22) tenure (PJ: .18, PO: .03, PG:.06, PS: .09) turnover (PJ: −.08, PO: −.14). Relationships with overallperformance were comparable for PJ (.20), PG (.19), and PS fit (.18);the relationship with PO fit was substantially smaller (.07). Contextualperformance had comparable relationships with PO (.27) and PG fit (.23);reliable estimates for PJ and PS fit could not be determined because ofsample-size constraints.

Table 5 reports the meta-analysis results for the comparisons betweentypes of fit. All of the 80% credibility intervals did not include zero,indicating that the types of fit are positively related across settings. Inthe overall meta-analyses, the low percentage of variance accounted forby artifacts suggests that moderator effects are likely present. Contraryto our expectation, the strongest relationship was between PJ and PO fit(.72), followed by PO and PG (.54) fit, and then PJ and PG fit (.49). Theweakest relationships were with PS fit, having a corrected correlation of.46 with PO fit and .37 with PG fit. As expected, however, differentiatingthe PJ studies into needs–supplies or demands–abilities conceptualizationsshowed a weaker relationship between PO fit and demands–abilities PJ fit(.45) than with needs–supplies PJ fit (.73). Measurement strategy servedas a moderator for all conditions in which it could be reliably examined.Direct measures of PO–PJ relationships reported a substantially strongercorrelation (.74) versus indirect objective measures (.24); however, for POand PG fit, the effect was in the opposite direction (direct: .48, indirectobjective: .60).

Discussion

“Of all the issues in psychology that have fascinated scholars and prac-titioners alike, none has been more pervasive than the one concerning thefit of person and environment” (Schneider, 2001, p. 141). The basic de-marcation between PJ, PG, PO, and PS fit provides a meaningful way ofassessing how fit with various aspects of the work environment influencesindividuals’ attitudes and behaviors. The four types of fit were only mod-erately related to each other, and even less so when assessed with indirect


measures. These results underscore the uniqueness of each type of fit andthe ability of individuals to discern among aspects of their work environ-ment when assessing fit. In particular, the relationships between PS fit andthe other types were small, suggesting that employees do not view supe-riors as isomorphic representations of the organization. It is worth notingalso that in studies that assessed multiple types of fit, most report a uniqueprediction attributable to each type of fit (e.g., Kristof-Brown et al., 2002;O’Reilly et al., 1991). Taken together, these results suggest that overallPE fit is a multidimensional concept consisting of multiple subtypes of fit(Law, Wong, & Mobley, 1998).

It is clear from these results that fit matters. Even during relativelybrief preentry encounters, attitudes and decisions are strongly influencedby various types of fit. As anticipated, attitudes about various aspects ofthe environment were most strongly related to the corresponding type offit. Job satisfaction was most strongly influenced by PJ fit, organizationalcommitment by PO fit, satisfaction with coworkers by PG fit, and satisfac-tion with supervisor with PS fit. These results provides additional supportfor the capability of individuals to, at least partially, compartmentalizetheir reactions to various aspects of their work environment.

Although we anticipated some compartmentalization, the weak resultsfor PG fit on the more general attitudes of job satisfaction, organizationalcommitment, and intent to quit surprised us. Emerging theories that em-phasize fit and composition effects for groups (e.g., Arrow, McGrath, &Berdahl, 2000; Beersma et al., 2003) would suggest that PG fit should bemore influential. One potential explanation for the weak findings is theheavy reliance on objective, personality-based measures of PG fit. Thecurrent paucity of PG fit studies does not allow us to explore modera-tors; however, the results from PO fit studies suggest that these types ofmeasures represent the weakest types of fit effects. Researchers of PG fitmust also grapple with the difficult question of how to best assess grouppsychological characteristics. Studies suggesting that the simple averageof a group is not the best indicator of its properties (e.g., Barrick, Stewart,Neubert, & Mount, 1998) raise questions about how best to assess thevalues or personality of a group. Recent work focused on assessing tra-ditionally individual-level constructs, such as personality, across levelsof analysis appears promising (e.g., Hofmann & Jones, 2003; Morgeson& Hofmann, 1999). These issues become particularly problematic whenfaultlines (Lau & Murnighan, 1998) sharply divide members. Shaw (2004)provides some direction for assessing faultlines in groups, but the impli-cations for studies of fit are not yet clear.

In both preentry and postentry contexts stronger relationships were re-ported for attitudinal criteria than for behaviors. This may be due, in part,to common method bias inflating self-reported fit–attitude relationships.


A second explanation is that there are considerable barriers to acting onexperiences of fit or misfit at work (Griffeth et al., 2001; Mitchell et al.,2001). The consequences of withdrawing from work, performing poorly,or leaving the organization can be severe, making it likely that attitudeswill be influenced by fit well before behaviors are changed. Despite thesebarriers, these results provide evidence that various types of fit do influ-ence behavioral outcomes including performance and turnover. Overallperformance was only weakly associated with PO fit but more stronglywith the other three types of fit. This is understandable for PJ fit becauseof the emphasis on job proficiency; however, the association with PGand PS fit was unexpected. This may reflect the growing importance ofteams as the fundamental work unit in organizations (Lawler, Mohrman, &Ledford, 1995) and the impact of supervisors’ input on overall perfor-mance assessments. PG fit also emerges as a relevant predictor of con-textual performance, as does PO fit. As organizations continue to askemployees to do more with less, using PG and PO fit to hire and retainindividuals who contribute beyond job requirements may provide a com-petitive advantage.

The results for tenure and turnover seem to tell conflicting stories. PJfit had the strongest association with tenure, and even that was a modestrelationship. However, PO fit had a slightly stronger impact on turnoverthan did PJ fit. One possible explanation for these results is the timing ofthe measures and Schneider’s (1987) ASA model. Tenure was measuredconcurrently in all studies, whereas turnover was collected anywhere from3 months to 2 years after the fit assessments. Although the ASA modelsuggests that attraction and selection will help screen out people who donot have a good PO fit, organizations are required to hire based on job-relevant qualifications. This implies that the PJ fit of employees at anygiven time should be high but that some people with low or modest PO fitwill likely be hired. If employees have poor PJ fit, they may try to developtheir skills, change jobs internally, or even be demoted. However, if theyhave a poor PO fit, the ASA model implies that they will eventually leavethe company, making PO fit a better predictor of eventual turnover. In amarketplace where human talent is becoming increasingly viewed as a keycompetitive advantage (Reichheld, 1996), organizations may find PO fit auseful tool for reducing turnover.

Moderators

For PJ and PO fit there were sufficient studies to examine severalpotential moderators. One of these, conceptualization of fit, was foundto influence most fit–outcome relationships. In almost every case thecomplementary form of needs–supplies fit has the greatest impact on


individual attitudes and behavior. This was followed closely by supple-mentary fit. These results are consistent with recent research by Cableand Edwards (2004), which demonstrates that psychological need ful-fillment and value congruence play unique and relatively equal roles inaffecting work attitudes. Alternatively, demands–abilities fit had a sub-stantially weaker effect on attitudes. It did, however, show relatively equaleffects on strain and performance as the other forms of fit. This contradictsMuchinsky and Monahan’s (1987) notion that complementary demands–abilities fit is only relevant to organizational outcomes. Taken together,these results reinforce the importance of considering multiple concep-tualizations, as well as multiple types of fit, when predicting individualoutcomes at work.

Consistent with the results for fit conceptualization, the various contentdimensions on which fit can be assessed also proved to be important mod-erators. For both PJ and PO fit, studies that assessed fit across a varietyof content dimensions (e.g., values, personality, needs, KSAs) reportedstronger relationships with criteria. However, in the case of PO fit, values-only measures produced only slightly weaker effects. This finding lendscredence to Chatman’s (1989) emphasis on values as the crux of person–culture fit. Goals also appear to hold some promise as measures of PO fit,although their relationship with job satisfaction was less than values-basedmeasures. The results for job satisfaction also strongly call into questionthe use of overall personality similarity as an indicator of PO fit. Althoughthere were not enough studies to conduct a reliable moderator analysis,studies of PS fit based on personality similarity also reported lower-than-average effect sizes (e.g., Schaubroeck & Lam, 2002; Smith, 2002). Aspreviously articulated, this may reflect the notion that complementarity,rather than similarity, may be desirable on some traits (e.g., Kristof-Brownet al., in press; Smith, 2002). Future studies of personality-based fit areadvised to use measures that are capable of assessing various conceptual-izations of fit at the trait level, rather than overall personality profiles.

Measurement strategy was found to be one of the most importantmoderators of PJ and PO fit effects. In almost all cases, direct measuresgenerated stronger results than indirect measures. This is consistent withVerquer et al.’s (2003) finding that individuals’ overall assessment of fit isthe best predictor of outcomes. Although stronger effects may be found byassessing perceived fit, this does not necessarily imply that direct measure-ment is optimal. Direct assessments of perceived fit are more susceptibleto a common method bias than other measures and shed little light onthe characteristics that underlie fit perceptions. It is worth noting that thedifferences in direct and indirect measures of fit are greatest in the preen-try context. The mean difference between direct and indirect measures ofPO fit in this context is .45. This suggests that although applicants and


recruiters are strongly influenced by perceptions of PO fit during re-cruitment and selection, these perceptions have little, if any, connectionwith reality. Whether fit perceptions are generated based on organization-controlled recruitment materials (e.g., Cable et al., 2000; Dineen, workingpaper; Dineen et al., 2002; Saks & Ashforth, 1997) or a short interview(Cable & Judge, 1997; Higgins, 2000; Kristof-Brown, 2000), they arebased on very limited information. Indeed, there is some evidence to sug-gest that recruiters’ perceptions of applicant PO fit are more likely toreflect the “similar-to-me” bias than true fit with the organization’s culture(Adkins et al., 1994; Howard & Ferris, 1996).

With regard to the common method bias, our comparisons consistentlydemonstrated that studies using all common raters had higher effect sizesthan those using partially common raters. These, in turn, generally re-ported stronger effects than those with no common source reporting. Inaddition, in studies of fit–attitude relationships, there appears to be a mod-est bias created by measuring fit and the criteria at the same point in time.Taken together, this evidence suggests that common-method bias is likelyaugmenting most reported fit relationships. How influential this bias is,however, we cannot definitively say because it is confounded with dif-ferences between perceived, subjective, and objective fit. These conceptsdiffer dramatically in terms of what environment is assessed, not just howit is measured. A compelling question for fit researchers is whether usingcommon raters or concurrent measurement compromises the integrity ofthe reported relationships, or whether it reflects the reality of how peo-ple’s attitudes are influenced by fit as they experience it. There is a stronghistorical argument in interactional psychology that people can only beinfluenced by fit with the environment as they perceive it (e.g., Caplan,1987; Endler & Magnussen, 1976; French et al., 1982). Therefore, al-though using a common source may increase the relationship between fitand outcomes, this may reflect reality rather than artifactual bias. Thereis no simple answer to what approach is best. However, using indirectsubjective measures and collecting fit and criteria data at different pointsin time should reduce the impact of artifactual biases, while still havingthe benefit of assessing fit with the experienced environment.

Finally, the moderator results also shed some light on the role thatsampling plays in reported fit–outcome relationships. Despite some highereffect sizes for single-unit studies of organizational commitment, effectsizes were generally comparable with multi-unit studies (particularly whenVancouver and Schmitt [1991] was excluded). These equivocal results mayreflect variability in culture strength across single-organization samples,something that would also be captured in multi-unit samples. Alterna-tively, they may reflect the strongly personal nature of fit assessments,which vary as widely within as between organizations. In either case, the


use of single-organization or job samples does not appear to have signifi-cantly reduced the strength of reported relationships. In contrast, how theenvironment is assessed in studies of objective fit does appear to make adifference. Specifically, the aggregation of employee characteristics pro-duced stronger fit effects than treating the organization as a unique entity.In virtually every case both types of measures were aggregates (i.e., thecomposite of multiple-members’ ratings of the environment or of them-selves). Therefore, these results cannot be attributed to the greater reliabil-ity of aggregated measures. It is more likely that they reflect the high de-gree of inaccuracy in people’s perceptions of organizational characteristics(Cable, Aiman-Smith, Mulvey, & Edwards, 2000) or, that they truly reflectSchneider’s proposition that “the people make the place.”

Although the credibility intervals around the effect size estimates sug-gested that most were generalizable, a separate discussion of Vancouverand Schmitt’s (1991) results is warranted. Vancouver and Schmitt (1991)examined the fit between the goals of secondary school teachers and theirschool’s goals, as reported by their principals and coworkers. Contributing13,388 to the overall N, this study reported effect sizes substantially lowerthan the overall mean effects. Therefore, when all analyses were run ex-cluding this study, the average effects sizes increased .06−.12. This is lessthan the impact reported by Verquer et al. (2003), due in part to the largerk and N in this study, but is still noteworthy. This may be attributable tothe smaller impact of goal-based fit versus values-based fit, or the use ofindirect objective measures. Another possibility is that the sample was insome way unique. One other study examining secondary school teachers(Erdogan, Kraimer, & Liden, 2004) also presented lower-than-average ef-fect sizes for PO fit, this time assessed using a value similarity index. Afinal possibility is that because teachers were asked to report “the impor-tance of the same 14 goals for the school” (Vancouver & Schmitt, p. 342),rather than how important the goals were to them personally, the goal con-gruence index may reflect agreement more than fit. Continued researchexploring these alternative explanations is encouraged to better interpretVancouver and Schmitt’s results.

Future Research

While conducting this meta-analysis, we became aware of severalnoticeable gaps in the fit literature. First, despite the acknowledgementthat multiple conceptualizations of fit exist, there has been surprisinglylittle research focused on validating multidimensional approaches. Thethree-dimensional model (PO value congruence, PJ needs–supplies fit, PJdemands–abilities fit) proposed by Cable and DeRue (2002) is a step to-ward this type of theory. However, by not incorporating PG or PS fit, as


well as other types of PO fit such as personality and goal similarity, theirmodel leaves room for expansion. Now that more is known about varioustypes of fit, perhaps it is time to revisit the field’s earlier notion of anoverall assessment of PE fit, whether it be formative (i.e., composed byvarious independent types of fit) or reflective (i.e., one overarching con-struct that influences perceptions of various types of fit; Diamantopoulos& Winklhofer, 2001). To move in this direction, however, more attentionmust be paid to underexplored areas of fit, including all forms of PG andPS fit, as well as complementary forms of PO fit and supplementary formsof PJ fit. Moreover, exploration of how various types of fit influence eachother over time and how fit is influenced by family issues (e.g., Edwards& Rothbard, 1999) will be important for establishing a more completemodel of PE fit.

A second area requiring attention is fit as a dependent variable. Specif-ically, a better understanding of what it means to people to “fit” and themechanisms that stimulate fit are long overdue. Edwards (1993, 1994)fundamentally altered fit research with the introduction of polynomial re-gression analysis as a more precise way to assess the impact of fit andmisfit. Results of these studies suggest that criteria are often predicted byrelationships that do not adhere to symmetrical notions of fit. Most sug-gest that fit at higher levels of a characteristic is generally superior to fitat lower levels and that misfit effects are asymmetrical (e.g., Edwards &Harrison, 1993; Livingstone et al., 1997; Slocombe & Bluedorn, 1999).It is interesting to note that some of the earliest theorizing on PE fit (e.g.,Caplan, 1983; Kulka, 1979) suggested asymmetrical effects of misfit andfit that are actually consistent with recent polynomial regression findings.There is a clear need to revisit this research and explore the social compar-ison process underlying people’s perceptions of fit. Polynomial regressionstudies that use perceived PE fit as the dependent variable are one viableapproach, as are qualitative explorations of individuals’ cognitive schemasregarding fit.

Regarding mechanisms that facilitate fit, most authors continue to relyon Schneider’s ASA model to explain how high levels of fit are generatedin organizations, despite the fact that this model was developed to explainorganizational homogeneity (Schneider et al., 1995). Longitudinal inves-tigations of what prompts changes in individuals’ levels of fit over time,from preentry to turnover, are needed to take Schneider’s theory to theindividual level. One set of studies that does address fit as an outcome arethose that examine how socialization practices influence subsequent levelsof fit (e.g, Cable & Parsons, 2001; Chatman, 1991; Grant & Bush, 1996).What is lacking, however, is a comprehensive theory of how individual ac-tions and organizational practices during and immediately following entryimpact both perceived and actual levels of fit. Without a good theory to


explain this process, it is difficult to predict when individuals will becomemore like their coworkers and organizations, when jobs will change toreflect individuals’ characteristics, when cognitive distortion will be usedto change perceived but not actual fit, or when attrition with be the primarymeans by which greater levels of fit will be achieved. Kraimer (1997) pro-posed a framework of responses to socialization that included value beliefstrength and initial values congruence as determinants of value change.Similarly, the concept of job-role differentiation (Ilgen & Hollenbeck,1990), which addresses when individuals alter the tasks they are expectedto perform through role-making, may have implications for when individ-uals will proactively address situations of misfit.

There is also need for future research on personal and situational char-acteristics that moderate fit–outcome relationships. Current results suggestthat fit is most influential if the dimensions measured are important to theindividual (similar to the concept of facet importance in job satisfactionresearch; Edwards, 1996; McFarlin & Rice, 1992) and if the individualhas high self-esteem (Dineen et al., 2002; Orpen, 1974). A few studieshave examined personality traits (Miles & Perrewe, 1999; Shantz, 2003)and demographic variables (Lovelace & Rosen, 1996) but have found littlesupport for their role as moderators. Even fewer studies have examined theimpact of situational moderators on fit effects; however, there is prelimi-nary evidence that relationships with managers and coworkers (Erdoganet al., 2004; Morris, 1995; Strauss et al., 2001) and even job performance(Shaw & Gupta, 2004) may influence the nature of the fit’s relationshipwith outcome variables. This area seems particularly in need of research,as many managers face the challenge of minimizing the effects of poor fitfor their employees.

Perhaps because of its complexities, fit also tends to be examined in-dependently, rather than within the context of other meaningful predictorsof work outcomes. Mitchell’s work (Mitchell et al., 2001; Mitchell &Lee, 2001), which incorporates fit into a broader model of turnover, andMasterson and Stamper’s (2003) theory of perceived organizational mem-bership are noteworthy examples of a fourth direction for fit research.Masterson and Stamper classify both PO fit and psychological contractsas measures of the “fulfillment of needs” between employees and orga-nizations in social exchange relationships. However, little research hasexamined the relationship between these concepts, or how the study ofone might inform the other. One related concept is that of perceived or-ganizational support (POS), defined as global beliefs about how much anorganization values its employees and their contributions (Eisenberger,Huntington, Hutchinson, & Sowa, 1986). A small number of studies haveexamined the relationship between PO fit and POS with the results rang-ing anywhere from −.03 (Erdogan et al., 2004) to .53 (Cable & DeRue,


2002). More research investigating the similarities and differences in theseconcepts is necessary.

Methodologically too, there are areas that warrant additional research.First, research that assesses objective fit is needed to better understandhow levels of analysis issues affect studies of fit. In particular, comparingindividual-level (i.e., when P and E are both reported by individuals) versuscross-level (i.e., when P is reported by an individual, but E is an aggregateof others’ ratings) is merited. Although common in studies of PS fit, rarelyhave individual-level reports been used in PG or PO fit studies (exceptionsare Becker, 1992; Tikanmaki, 2001; Vancouver & Schmitt, 1991). Ratherthan dismissing this approach, comparative research is called for. Second,research comparing the effects of simultaneous assessments of multiplekinds of fit is needed. Most studies have assumed that various types of fithave additive effects (i.e., good fit on one dimension can compensate forpoor fit on another). However, poor fit with one aspect of the environmentmay spill over into other areas, or that they may otherwise interact (Jansen& Kristof-Brown, 2002). Only by examining multiple types of fit longi-tudinally and within the same investigation can these types of spilloverrelationships be discerned.

Limitations and Strengths

Due to the fact that studies using objective criteria, including tenure,turnover, withdrawal, job offer, and job acceptance, did not report reli-abilities for the variables, analyses that include them are not fully cor-rected for measurement error. Although the reliabilities for such crite-ria are typically high and as such would not have a large impact onthe true score correlations, the meta-analytic estimates of fit relation-ships with these dependent variables are slightly conservative. In addi-tion, because corrections for dichotomization were not able to be per-formed when turnover was the criterion, the meta-analyses for PJ fitand PO fit with turnover are also slightly attenuated. A limitation ofthis meta-analysis is also the number of studies available for moderatoranalyses. Although every effort was made to collect all relevant studies,there simply are not enough yet to examine moderators of PG and PS fitrelationships.

Perhaps, most importantly, we acknowledge that our results reflect thestrengths and weaknesses of every study in our sample. In his qualitativereview of the PJ and PV fit literature, Edwards concluded that “nearly everystudy reviewed contained methodological flaws that seriously threaten theinterpretability of the results obtained.” (Edwards, 1991, p. 290). Specif-ically, studies using PSIs often suggest fit relationships that are not fullysupported when polynomial regression is used to reanalyze them (e.g.,


Edwards, 1996; Kalliath et al., 1999). Although we acknowledge the va-lidity of this concern, there are several reasons we do not believe thatit is compelling enough to dismiss a tradition of research that continuesto stimulate interest more than half a century since its inception. First,in most cases the polynomial regression results expand upon, but do notnullify, the fundamental notion that the fit between P and E is benefi-cial to individuals. We now know that fit at high levels of a characteristicis generally better than fit at low levels and that certain types of mis-fit are worse than others. However, these results have not negated theconclusion that fit is typically preferable to misfit. Second, meta-analysisallows measurement error due to unreliability in direct and indirect mea-sures of fit, as well as in the criteria of interest, to be statistically cor-rected. This strengthens the conclusions that can be drawn from thesestudies, versus the uncorrected results reported in single studies. Finally,progress in any field can only be made by recognizing limitations in pastresearch, correcting them when feasible, and comparing the results withthose generated by new advances. Even if our research only serves as abaseline against which future studies of fit are compared, it is an impor-tant part of the generation and dissemination of knowledge regarding fitphenomena.

These limitations are countered by some important strengths. Thismeta-analysis calculated empirical estimates on four major conceptual-izations of fit and a wide range of attitudinal and behavioral criteria. Weprovide an updated and comprehensive review of the literature (Edwards,1991; Kristof, 1996; Verquer et al., 2003). Compared to the meta-analysisof PO fit by Verquer et al. (2003), which included approximately 60 ef-fect sizes across three criteria, we analyzed 450 effect sizes across 18outcome variables for PO fit alone. This large jump was due, in part, tothe extensive array of sources utilized. In addition to searching the avail-able databases, nearly a decade of conference programs was searched andtwo dozen prominent fit researchers were contacted for additional stud-ies. Finally, the “snowballing” technique was used by searching throughthe references of primary fit studies and fit reviews to find additionalsources of data. We also corrected for unreliability in the predictors, whichVerquer et al. (2003) did not do, which allowed us to examine relationshipsat the construct level. In addition, in line with recent trends in fit research,we summarize studies using the polynomial regression technique that werenot discussed by Verquer et al.

Implications

Although there appears to be no perfect way to assess fit, there arelessons to be learned from past approaches. First, scholars should consider


both the criteria (attitudes vs. behaviors), the context (preentry vs. posten-try), and the underlying mechanisms by which they believe fit is operatingwhen selecting a measurement approach. There may be times when di-rect assessment of fit is preferable. For example, if fit is being examinedas an endogenous variable, such as a mediator of another relationship, itmay be beneficial to assess the concept directly to capture the cognitivecompatibility assessment at its broadest level. Under other conditions itmight be more meaningful to assess fit indirectly to allow comparisonson particular values or goals, such as when a culture change program hasbeen implemented. In these cases the polynomial regression approach iswell advised. Second, it is important to recognize that all measures are notcreated equal. Our results suggest that when possible, multiple concep-tualizations and content dimensions should be specified in the measures,regardless of whether they are direct or indirect. Rather than asking fora global assessment of “How well do you fit?” scales should provideguidance on the dimensions along which fit is to be assessed. Finally, re-searchers must ask themselves how important it is to understand the exactform of the relationship underlying fit perceptions. When more precisionis necessary, as in cases where excess versus deficiency are expected tohave distinct results, the polynomial approach is recommended. When amore holistic assessment of similarity is sufficient, PSIs may provide ad-equate information. When deciding upon a measurement strategy, no oneapproach can meet all needs. Researchers must consider their researchquestion, sample characteristics, and prior evidence regarding the spe-cific fit relationship before determining how to assess fit in a particularinvestigation.

These results also have implications for management practices. First,we present conclusive evidence that fit matters to applicants, recruiters,and employees. It influences their attitudes, decisions, and behaviors inthe work domain. Yet, our results also suggest that fit is a complicatedconcept, with multiple types of fit influencing all outcomes. Therefore,managers wishing to maximize the benefits of fit are encouraged to attendto the various aspects of the environment with which fit may occur. It is notenough to increasingly refine a job description or indoctrinate employeesinto a company’s culture. Instead, a multifaceted approach that involves thedemands and supplies of jobs, coworker characteristics, and organizationalelements is needed. Broad frameworks such as the proposed content andconsequences of organizational socialization practices proposed by Chao,O’Leary-Kelly, Wolf, Klein, and Gardner (1994) may be used to guidemanagers to specific techniques aimed at increasing the levels of actualand perceived fit on particular dimensions. Managers should also attendto the relative impact of various types of fit on outcomes. For example, atremendous amount of time is spent articulating the job requirements for


positions, but the demands–abilities fit has a minor impact on attitudes andeven performance when compared with the needs–supplies fit. Recruitersand managers would be wise to highlight what jobs and organizationsprovide to maximize fit perceptions. Similarly, pairing individuals up withsimilar others is advisable for enhancing fit assessments.

More than 10 years ago, Bowen, Ledford, and Nathan (1991) encour-aged managers to consider both PO and PJ fit during organizational entrybecause each may be related to different outcomes. Our results reinforcethe utility of such an approach. As the business world continues to requiremanagers to do more with less, criteria such as turnover and contextualperformance become important criteria. Selection techniques that assessmultiple types of fit need to be developed and validated against the criterionthey are most likely to affect.

Yet, there is little evidence that perceived fit during preentry periods isan accurate reflection of objective fit relationships. This implies that de-cisions by both applicants and recruiters are being made based on flawed(or at least uninformed) information. To improve decision making dur-ing preentry periods, fit-based instruments with demonstrated criterion-related validity must be developed. Recently, commercial instrumentshave been developed and are being offered to managers as ways to as-sess PO fit, but little research has been done on these measures (for anexception see Hambleton, Kallieth, & Taylor, 2000). Most also are lim-ited by a unidimensional perspective, focusing solely on one type of fitand/or one content dimension. Research will be needed on the conse-quences of testing for fit during hiring. Negative applicant reactions, fak-ing, and adverse impact are all issues that need to be better researchedbefore managers whole-heartedly embrace a “hire for the company”attitude.

A final implication stems from the relative inaccuracy of perceived fitassessments. This is particularly true for the PO fit, which an employeemay not be able to assess simply because organizational characteristicsmay be difficult to identify. For example, perceived fit and actual fit onethical values may be distally related because managers spend little timeexplicitly discussing their moral principles (Trevino, Hartman, & Brown,2000). From the beginning of the recruitment process through long-termemployment, managers should pay attention to how clearly they are com-municating work unit and organizational values. This should aid in theattraction, hiring, and retention of individuals who share those values andare inspired by an organization that reinforces them. This approach maylead people who do not fit to leave the organization, so special attentionshould be paid to maintaining a healthy level of diversity in order to avoidthe drawbacks associated with excessive homogeneity (Schneider et al.,1995).


Conclusion

In sum, research on PE fit spans more than 100 years, and interestin the field continues to grow. The number of dissertations conductedrecently in the specific domains of PJ, PG, and PO fit suggests an upwardtrajectory for such research. We hope that this study, while raising newquestions, also provides some answers for those interested in what wealready know about how PE fit influences work-related outcomes. Resultsprovide strong evidence for the importance of multiple types of fit forwork-related attitudes and behaviors.

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